Critical review of prostate cancer predictive tools

Prostate cancer therapy outcome prediction: are miRNAs a suitable guide for therapeutic decisions?

BACKGROUND

Radical prostatectomy, radiotherapy, chemotherapy, and androgen-deprivation therapy are among the most common treatment options for different forms of prostate cancer (PCa). However, making therapeutic decisions is difficult due to the lack of reliable prediction markers indicating therapy outcomes in clinical practice. The involvement of miRNAs in all mechanisms of the PCa development and their easy detection characterize them as attractive PCa biomarkers. Although there are extensive data on the role of miRNAs in PCa therapy resistance and sensitivity development, the issues of whether they could be used as a guide for therapy choice and, if so, how we can progress toward this goal, remain unclear. Thus, generalizable reviews and studies which summarize, compare, and analyze data on miRNA involvement in responses to different types of PCa therapies are required.

OBJECTIVES

Data on the involvement of miRNAs in therapy responses, on the role of cross-miRNA expression in different therapies, and on miRNA targets were analyzed in order to determine the miRNA-related factors which can lend perspective to the future development of personalized predictors of PCa sensitivity/resistance to therapies.

MATERIALS AND METHODS

The data available on the miRNAs associated with different PCa therapies (resistance and sensitivity therapies) are summarized and analyzed in this study, including analyses using bioinformatics resources. Special attention was dedicated to the mechanisms of the development of therapy resistance.

RESULTS AND DISCUSSION

A comprehensive combined analysis of the current data revealed a panel of miRNAs that were shown to be most closely associated with the PCa therapy response and were found to regulate the genes involved in PCa development via cell proliferation regulation, epithelial-mesenchymal transition (EMT), apoptosis, cell-cycle progression, angiogenesis, metastasis and invasion regulation, androgen-independent development, and colony formation.

CONCLUSION

The selected miRNA-based panel has the potential to be a guide for therapeutic decision making in the effective treatment of PCa.

Prognostic role of platelet-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio in patients with non-metastatic and metastatic prostate cancer: A meta-analysis and systematic review

Objective

To analyze data available in the literature regarding a possible prognostic value of the platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) in prostate cancer (PCa) patients stratified in non-metastatic and metastatic diseases.

Methods

A literature search process was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. In our meta-analysis, the pooled event rate estimated and the pooled hazard ratio were calculated using a random effect model.

Results

Forty-two articles were selected for our analysis. The pooled risk difference for non-organ confined PCa between high and low NLR cases was 0.06 (95% confidence interval [CI]: -0.03-0.15) and between high and low PLR cases increased to 0.30 (95% CI: 0.16-0.43). In non-metastatic PCa cases, the pooled hazard ratio for overall mortality between high and low NLR was 1.33 (95% CI: 0.78-1.88) and between high and low PLR was 1.47 (95% CI: 0.91-2.03), whereas in metastatic PCa cases, between high and low NLR was 1.79 (95% CI: 1.44-2.13) and between high and low PLR was 1.05 (95% CI: 0.87-1.24).

Conclusion

The prognostic values of NLR and PLR in terms of PCa characteristics and responses after treatment show a high level of heterogeneity of results among studies. These two ratios can represent the inflammatory and immunity status of the patient related to several conditions. A higher predictive value is related to a high NLR in terms of risk for overall mortality in metastatic PCa cases under systemic treatments.

Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma

Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.

Deep-learning Method for the Prediction of Three-Dimensional Dose Distribution for Left Breast Cancer Conformal Radiation Therapy

AIMS

An increase in the demand of a new generation of radiotherapy planning systems based on learning approaches has been reported. At this stage, the new approach is able to improve the planning speed while saving a reasonable level of plan quality, compared with available planning systems. We believe that new achievements, such as deep-learning models, will be able to review the issue from a different point of view.

MATERIALS AND METHODS

The data of 120 breast cancer patients were used to train and test the three-dimensional U-Res-Net model. The network input was computed tomography images and patients' contouring, while the patients' dose distribution was addressed as the output of the model proposed. The predicted dose distributions, created by the model for 10 test patients, were then compared with corresponding dose distributions calculated by a reliable treatment planning system. In particular, the dice similarity coefficients for different isodose volumes, dose difference and mean absolute errors (MAE) for all voxels inside the body, Dmean, D98%, D50%, D2%, V95% for planning target volume and organs at risk were calculated and were statistically analysed with the paired-samples t-test.

RESULTS

The average dose difference for all patients and voxels in body was 0.60 ± 2.81%. The MAE varied from 3.85 ± 6.65% to 8.06 ± 10.00%. The average MAE for test cases was 5.71 ± 1.19%. The average dice similarity coefficients for isodose volumes was 0.91 ± 0.03. The three-dimensional gamma passing rates with 3 mm/3% criteria varied from 78.99% to 97.58% for planning target volume and organs at risk, respectively.

CONCLUSIONS

The investigation showed that a deep-learning model can be applied to predict the three-dimensional dose distribution with optimal accuracy and precision for patients with left breast cancer. As further study, the model can be extended to predict dose distribution in other cancers.

Pathology Data-Based Risk Group Stratification Is Equivalent to That Obtained by Oncotype DX Testing in Prostatic Adenocarcinoma

CONTEXT.—

Low-risk (Gleason score 3 + 3 = 6) and intermediate-risk (Gleason score 3 + 4 = 7) prostate carcinoma cases diagnosed on needle biopsies are frequently referred for gene expression studies such as Oncotype DX to help validate the risk. Risk assessment helps in determining prognosis and therapeutic decision making.

OBJECTIVE.—

To determine if addition of molecular testing is necessary, by evaluating its correlation with risk stratification provided by pathology report (Gleason score, Grade Group, proportion of positive cores) and serum prostate-specific antigen (PSA) level.

DESIGN.—

Our institutional database was searched for cases that had Oncotype DX testing after prostate biopsy. The final risk category determined by molecular testing was compared to the risk stratification predicted by the pathology report and serum PSA levels. Cases were classified as concordant if they fell under the same National Comprehensive Cancer Network risk and recommended initial therapy group. Follow-up information on discordant cases was obtained and used to determine if risk stratification by molecular testing was superior to that obtained from the clinicopathologic data.

RESULTS.—

A total of 4967 prostate biopsies (2015-2020) were screened. Of these, 131 prostate carcinoma cases (2.6%) had Oncotype DX testing and 111 of 131 cases (85%) had follow-up information. There was risk stratification concordance in 93 of 111 cases (84%). All 18 of 111 cases (16%) that were discordant had a follow-up course that matched the risk predicted by pathology data and serum PSA.

CONCLUSIONS.—

Risk stratification provided by information in the pathology report on routine biopsy assessment coupled with the serum PSA level is equivalent to that obtained by Oncotype DX testing.

A Personalized Clinical Dynamic Prediction Model to Characterize Prognosis for Patients With Localized Prostate Cancer: Analysis of the CHHiP Phase 3 Trial

PURPOSE

The CHHiP trial assessed moderately hypofractionated radiation therapy in localized prostate cancer. We utilized longitudinal prostate-specific antigen (PSA) measurements collected over time to evaluate and characterize patient prognosis.

METHODS AND MATERIALS

We developed a clinical dynamic prediction joint model to predict the risk of biochemical or clinical recurrence. Modeling included repeated PSA values and adjusted for baseline prognostic risk factors of age, tumor characteristics, and treatment received. We included 3071 trial participants for model development using a mixed-effect submodel for the longitudinal PSAs and a time-to-event hazard submodel for predicting recurrence of prostate cancer. We evaluated how baseline prognostic factor subgroups affected the nonlinear PSA levels over time and quantified the association of PSA on time to recurrence. We assessed bootstrapped optimism-adjusted predictive performance on calibration and discrimination. Additionally, we performed comparative dynamic predictions on patients with contrasting prognostic factors and investigated PSA thresholds over landmark times to correlate with prognosis.

RESULTS

Patients who developed recurrence had generally higher baseline and overall PSA values during follow-up and had an exponentially rising PSA in the 2 years before recurrence. Additionally, most baseline risk factors were significant in the mixed-effect and relative-risk submodels. PSA value and rate of change were predictive of recurrence. Predictive performance of the model was good across different prediction times over an 8-year period, with an overall mean area under the curve of 0.70, mean Brier score of 0.10, and mean integrated calibration index of 0.048; these were further improved for predictions after 5 years of accrued longitudinal posttreatment PSA assessments. PSA thresholds <0.23 ng/mL after 3 years were indicative of a minimal risk of recurrence by 8 years.

CONCLUSIONS

We successfully developed a joint statistical model to predict prostate cancer recurrence, evaluating prognostic factors and longitudinal PSA. We showed dynamically updated PSA information can improve prognostication, which can be used to guide follow-up and treatment management options.

Positive family history as a predictor for disease outcomes after radical prostatectomy for nonmetastatic prostate cancer

Background

While family history (FHx) of prostate cancer (PCa) increases the risk of PCa, comparably less is known regarding the impact of FHx on pathologic and oncologic outcomes after radical prostatectomy (RP).

Methods

We retrospectively reviewed our multicenter database comprising 6,041 nonmetastatic PCa patients treated with RP. Patients with a FHx of PCa in one or more first-degree relatives were considered as FHx positive. We examined the association of FHx with pathologic outcomes and biochemical recurrence (BCR) using logistic and Cox regression models, respectively.

Results

In total, 1,677 (28%) patients reported a FHx of PCa. Compared to patients without FHx, those with, were younger at RP (median age of 59 vs. 62 years, p < 0.01), and had significantlymore favorable biopsy and RP histopathologic findings. On multivariable logistic regression analysis, positive FHx was associated with extracapsular extension (odds ratio [OR] 0.77, 95% confidence interval [CI] 0.66-0.90, p < 0.01; model AUC 0.73) and upgrading (OR 0.70, 95% CI 0.62-0.80, p < 0.01; model AUC 0.68). Incorporating FHx significantly improved the AUC of the base model for upgrading (p < 0.01). Positive FHx was not associated with BCR in pre- and postoperative multivariable models (p = 0.1 and p = 0.7); c-indexes of Cox multivariable models were: 0.73 and 0.82, respectively.

Conclusions

We found that patients with clinically nonmetastatic PCa who have positive FHx of PCa undergo RP at a younger age and have more favorable pathologic outcomes. Nevertheless, FHx of PCa did not confer better BCR rates, suggesting that FHx leads to potentially early detection and treatment without impact on BCR.

Evaluation of comprehensive complication index versus Clavien-Dindo classification in prediction of overall survival after radical cystectomy

PURPOSE

We aimed to evaluate the benefits of Comprehensive Complication Index (CCI) compared with the Clavien-Dindo Classification (CDC) in the reporting of radical cystectomy (RC) complications.

MATERIALS AND METHODS

We retrospectively analyzed post-operative complications of 251 consecutive RC patients between 2009 and 2021. Patient demographics and causes of mortality were noted. Oncologic outcomes included the recurrence, time to recurrence, cause of all deaths, and time to death. Each complication was graded with CDC and, corresponding and cumulative CCI calculated for each patient.

RESULTS

A total of 211 patients are included. Median patient age and follow-up time were 65 (IQR 60-70) years and 20 (IQR 9 - 53) months, respectively. The five-year recurrence and death rates were 39.3% (83/211) and 59.7% (126/211), respectively. Post-operative 521 complications were recorded. Patients with experienced any complication were 69.6% (147/211) and 45.0% (95/211) had > 1 complications. Thirty (14.2%) patients ended up with a cumulative CCI score that corresponded to a higher CDC grade. Severe complications calculated with CDC increased from 18.5% to 19.9% (p < 0.001) with cumulative CCI. Female gender, positive lymph node, and positive surgical margin, presence of severe CDC complication, and CCI score were significant independent predictive factors for overall survival (OS). The contribution of CCI to the multivariable model was 1.8% higher than CDC.

CONCLUSIONS

Cumulative morbidity reporting improved with the use of CCI compared to CDC. Both CDC and CCI are significant predictive factors for OS independent of oncologic predictive factors. Reporting the cumulative burden of complications with CCI is more predictive on oncologic survival than reporting complications with CDC.

Using PSMA imaging for prognostication in localized and advanced prostate cancer

The use of prostate-specific membrane antigen (PSMA)-directed applications in modern prostate cancer management has evolved rapidly over the past few years, helping to establish new treatment pathways and provide further insights into prostate cancer biology. However, the prognostic implications of PSMA-PET have not been studied systematically, owing to rapid clinical implementation without long follow-up periods to determine intermediate-term and long-term oncological outcomes. Currently available data suggest that traditional prognostic factors and survival outcomes are associated with high PSMA expression (both according to immunohistochemistry and PET uptake) in men with localized and biochemically recurrent disease. Treatment with curative intent (primary and/or salvage) often fails when PSMA-positive metastases are present; however, the sensitivity of PSMA-PET in detecting all metastases is poor. Low PSMA-PET uptake in recurrent disease is a favourable prognostic factor; however, it can be associated with poor prognosis in conjunction with high ¹⁸F-fluorodeoxyglucose uptake in metastatic castration-resistant prostate cancer. Clinical trials embedding PSMA-PET for guiding management with reliable oncological outcomes are needed to support ongoing clinical use.

The systemic inflammation response index: An independent predictive factor for survival outcomes of bladder cancer stronger than other inflammatory markers

PURPOSE

We aimed to evaluate the prognostic value of the preoperative systemic inflammation response index (SIRI) to predict the outcomes after open radical cystectomy (RC).

MATERIALS AND METHODS

We conducted a retrospective analysis of the institutional cystectomy database and identified 241 consecutive RC patients. Patient demographics and oncologic outcomes were noted. We calculated the SIRI as previously described (NeutrophilxMonocytes/Lymphocytes), based on the blood-tests at the day before surgery and a minimum >30-day later.

RESULTS

Median follow-up time was 20 months (interquartile range 9-52). Two, 3 and 5 years recurrence free (RFS) and overall survival (OS) rates were 60.6%, 57.1%, 48.9%, and 54.7%, 47.0%, 37.2%, respectively. Patients with preoperative SIRI >1.91 had significantly higher recurrence rates (P < 0.001) and lower OS (P < 0.001). For internal validation, we evaluated postoperative SIRI >1.91 (repeatability testing), and again found significantly higher recurrence rates (P < 0.001) and lower OS (P = 0.004). Persistently high SIRI increased the recurrence and death risk 5.79 and 2.87 fold, respectively. SIRI was also a significant independent predictive factor for RFS and OS in the multivariable cox regression analyses (P < 0.05). SIRI improved the discriminative ability of the models 1.5% to 4.2% and this was quite higher than other inflammatory markers (NLR, MLR, PLR, SII) in all models.

CONCLUSIONS

Patients with SIRI >1.91 had significantly higher recurrence and lower OS rates. The cut-off value is validated internally. SIRI is an independent predictive factor for RFS and OS. The contribution of SIRI in the cox models is higher than other inflammatory markers.

Comparative Prospective and Longitudinal Analysis on the Platelet-to-Lymphocyte, Neutrophil-to-Lymphocyte, and Albumin-to-Globulin Ratio in Patients with Non-Metastatic and Metastatic Prostate Cancer

PURPOSE

To prospectively evaluate the albumin/globulin ratio (AGR), neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) diagnostic and prognostic predictive value in a stratified population of prostate cancer (PC) cases.

METHODS

Population was divided based on the clinical and histologic diagnosis in: Group A: benign prostatic hyperplasia (BPH) cases (494 cases); Group B: all PC cases (525 cases); Group B1: clinically significant PC (426 cases); Group B2: non-metastatic PC (416 cases); Group B3: metastatic PC (109 cases). NLR, PLR, and AGR were obtained at the time of the diagnosis, and only in cases with PC considered for radical prostatectomy, determinations were also repeated 90 days after surgery. For each ratio, cut-off values were determined by receiver operating characteristics curve (ROC) analysis and fixed at 2.5, 120.0, and 1.4, respectively, for NLR, PLR, and AGR.

RESULTS

Accuracy in predictive value for an initial diagnosis of clinically significant PC (csPC) was higher using PLR (0.718) when compared to NLR (0.220) and AGR (0.247), but, despite high sensitivity (0.849), very low specificity (0.256) was present. The risk of csPC significantly increased only according to PLR with an OR = 1.646. The percentage of cases with metastatic PC significantly increased according to high NLR and high PLR. Accuracy was 0.916 and 0.813, respectively, for NLR and PLR cut-off, with higher specificity than sensitivity. The risk of a metastatic disease increased 3.2 times for an NLR > 2.5 and 5.2 times for a PLR > 120 and at the multivariate analysis.

CONCLUSION

PLR and NLR have a significant predictive value towards the development of metastatic disease but not in relation to variations in aggressiveness or T staging inside the non-metastatic PC. Our results suggest an unlikely introduction of these analyses into clinical practice in support of validated PC risk predictors.

Prognostic Value of Albumin to Globulin Ratio in Non-Metastatic and Metastatic Prostate Cancer Patients: A Meta-Analysis and Systematic Review

The aim of our meta-analysis is to analyze data available in the literature regarding a possible prognostic value of the albumin to globulin ratio (AGR) in prostate cancer (PC) patients. We distinguished our analysis in terms of PC staging, histologic aggressiveness, and risk of progression after treatments. A literature search process was performed (“prostatic cancer”, “albumin”, “globulin”, “albumin to globulin ratio”) following the PRISMA guidelines. In our meta-analysis, the pooled Event Rate (ER) estimate for each group of interest was calculated using a random effect model. Cases were distinguished in Low and High AGR groups based on an optimal cut-off value defined at ROC analysis. Four clinical trials were enclosed (sample size range from 214 to 6041 cases). The pooled Risk Difference for a non-organ confined PC between High AGR and Low AGR cases was −0.05 (95%CI: −0.12−0.01) with a very low rate of heterogeneity (I2 < 0.15%; p = 0.43) among studies (test of group differences p = 0.21). In non-metastatic PC cases, the pooled Risk Difference for biochemical progression (BCP) between High AGR and Low AGR cases was −0.05 (95%CI: −0.12−0.01) (I2 = 0.01%; p = 0.69) (test of group differences p = 0.12). In metastatic PC cases, AGR showed an independent significant (p < 0.01) predictive value either in terms of progression free survival (PFS) (Odds Ratio (OR): 0.642 (0.430−0.957)) or cancer specific survival (CSS) (OR: 0.412 (0.259−0.654)). Our meta-analysis showed homogeneous results supporting no significant predictive values for AGR in terms of staging, grading and biochemical progression in non-metastatic PC.

Dual-Tracer PET-Computed Tomography Imaging for Precision Radio-Molecular Theranostics of Prostate Cancer: A Futuristic Perspective

Dual/multi-tracer PET-computed tomography (CT) scan has been an interesting and intriguing concept and is promising in noninvasive and overall characterization of tumor biology and heterogeneity and has scientifically augmented the practice of precision oncology. In prostate carcinoma, particularly in metastatic castration-resistant prostate carcinoma setting, dual-tracer PET-CT can be potentially useful in selecting patients for chemotherapy, androgen deprivation therapy or prostate-specific membrane antigen (PSMA)-based peptide receptor radioligand therapy either as mono-therapy or as combination therapy, ascertaining differentiation status, staging/restaging, prognostication, and predicting progression/response. PSMA PET/CT has great potential as a "rule out" test in baseline staging, while being very useful in restaging and metastatic workup.

Joint models for dynamic prediction in localised prostate cancer: a literature review

BACKGROUND

Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer.

METHODS

We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes.

RESULTS

Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations.

CONCLUSIONS

Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.

A Systematic Review and Meta-Analysis of Prognostic Nomograms After UTUC Surgery

Background

Current guidelines recommend assessing the prognosis in high-risk upper tract urothelial carcinoma patients (UTUC) after surgery. However, no specific method is endorsed. Among the various prognostic models, nomograms represent an easy and accurate tool to predict the individual probability for a specific event. Therefore, identifying the best-suited nomogram for each setting seems of great interest to the patient and provider.

Objectives

To identify, summarize and compare postoperative UTUC nomograms predicting oncologic outcomes. To estimate the overall performance of the nomograms and identify the most reliable predictors. To create a reference tool for postoperative UTUC nomograms, physicians can use in clinical practice.

Design

A systematic review was conducted following the recommendations of Cochrane’s Prognosis Methods Group. Medline and EMBASE databases were searched for studies published before December 2021. Nomograms were grouped according to outcome measurements, the purpose of use, and inclusion and exclusion criteria. Random-effects meta-analyses were performed to estimate nomogram group performance and predictor reliability. Reference tables summarizing the nomograms’ important characteristics were created.

Results

The systematic review identified 26 nomograms. Only four were externally validated. Study heterogeneity was significant, and the overall Risk of Bias (RoB) was high. Nomogram groups predicting overall survival (OS), recurrence-free survival (RFS), and intravesical recurrence (IVR) had moderate discrimination accuracy (c-Index summary estimate with 95% confidence interval [95% CI] and prediction interval [PI] > 0.6). Nomogram groups predicting cancer-specific survival (CSS) had good discrimination accuracy (c-Index summary estimate with 95% CI and PI > 0.7). Advanced pathological tumor stage (≥ pT3) was the most reliable predictor of OS. Pathological tumor stage (≥ pT2), age, and lymphovascular invasion (LVI) were the most reliable predictors of CSS. LVI was the most reliable predictor of RFS.

Conclusions

Despite a moderate to good discrimination accuracy, severe heterogeneity discourages the uninformed use of postoperative prognostic UTUC nomograms. For nomograms to become of value in a generalizable population, future research must invest in external validation and assessment of clinical utility. Meanwhile, this systematic review serves as a reference tool for physicians choosing nomograms based on individual needs.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282596, identifier PROSPERO [CRD42021282596].

Radiation therapy compared to radical prostatectomy as first-line definitive therapy for patients with high-risk localised prostate cancer: An updated systematic review and meta-analysis

Objective

To present an update of the available literature on external beam radiation therapy (EBRT) with or without brachytherapy (BT) compared to radical prostatectomy (RP) for patients with high-risk localised prostate cancer (PCa).

Methods

We conducted a systematic review and meta-analysis of the literature assessing the survival outcomes in patients with high-risk PCa who received EBRT with or without BT compared to RP as the first-line therapy with curative intent. We queried PubMed and Web of Science database in January 2021. Moreover, we used random or fixed-effects meta-analytical models in the presence or absence of heterogeneity per the I² statistic, respectively. We performed six meta-analyses for overall survival (OS) and cancer-specific survival (CSS).

Results

A total of 27 studies were selected with 23 studies being eligible for both OS and CSS. EBRT alone had a significantly worse OS and CSS compared to RP (hazard ratio [HR] 1.38, 95% confidence interval [CI] 1.16–1.65; and HR 1.55, 95% CI 1.25–1.93). However, there was no difference in OS (HR 1.1, 95% CI 0.76–1.34) and CSS (HR 0.69, 95% CI 0.45–1.06) between EBRT plus BT compared to RP.

Conclusion

While cancer control affected by EBRT alone seems inferior to RP in patients with high-risk PCa, BT additive to EBRT was not different from RP. These data support the need for BT in addition to EBRT as part of multimodal RT for high-risk PCa.

Abbreviations: ADT: androgen-deprivation therapy; BT: brachytherapy; CSS: cancer-specific survival; HR: hazard ratio; MFS, metastatic-free survival; MOOSE: Meta-analyses of Observational Studies in Epidemiology; OR: odds ratio; OS: overall survival; PCa: prostate cancer; RR: relative risk; RP: radical prostatectomy; RCT: randomised controlled trials; (EB)RT: (external beam) radiation therapy

Comparison of short-term and long-term neoadjuvant hormone therapy prior to radical prostatectomy: a systematic review and meta-analysis

PURPOSE

This study aimed to evaluate the efficacy of long-term neoadjuvant androgen-deprivation therapy (ADT) before radical prostatectomy (RP).

METHODS

We conducted meta-analyses and network meta-analyses, which included randomized controlled trials that assessed patients with prostate cancer (PC) who received either short-term (<6 months) or long-term (≥6 months) neoadjuvant ADT before RP.

RESULTS

Thirteen articles with 2778 patients were eligible for analysis. Short-term neoadjuvant ADT was neither associated with biochemical recurrence (OR 1.19, 95% CI, 0.93-1.51, p = 0.17), metastasis (OR 0.73, 95% CI, 0.45-1.19, p = 0.21), nor overall mortality (OR 0.72, 95% CI 0.43-1.21, p = 0.22); no study investigated survival outcomes in patients on long-term neoadjuvant ADT. In terms of pathologic outcomes, long-term neoadjuvant ADT was significantly associated with a reduced risk of positive surgical margin (SM) and an increased rate of organ-confined disease (OCD) compared to short-term neoadjuvant ADT (OR 0.56, 95% CI 0.39-0.80, p = 0.001, and OR 1.48, 95% CI 1.10-1.99, p = 0.009, respectively). These findings were confirmed in the network meta-analyses. Meanwhile, only a non-significant trend favoring long-term neoadjuvant ADT was observed for pathologic complete response (OR 1.98, 95% Crl 1.00-3.93).

CONCLUSION

Long-term neoadjuvant ADT was associated with more favorable pathologic outcomes, but whether these findings translate into favorable survival outcomes still remains unproven due to very limited evidence. Since there are no reliable survival data, long-term neoadjuvant ADT before RP should not be used in clinical practice until more robust evidence arises from ongoing trials.

Optimization of clinical risk-factor interpretation and radiological findings with machine learning for PIRADS category 3 patients

BACKGROUND

Due to the low cancer-detection rate in patients with PIRADS category 3 lesions, we created machine learning (ML) models to facilitate decision-making about whether to perform prostate biopsies or monitor clinical information without biopsy results.

METHODS

In our retrospective, single-center study, 101 eligible patients with at least one PIRADS category 3 lesion but no higher PIRADS lesions underwent MRI/US fusion biopsies between September 2017 and June 2020. Thirty additional patients were included as the validation cohort from the next chronological period from June 2020 to October 2020. Our ML research was a supervised classification problem, with a binary output based on pathological reports of cancerous or benign tissue. The clinical inputs were age, prostate-specific antigen (PSA), prostate volume, prostate-specific antigen density (PSAD), and the number of previous biopsies. The radiology-report inputs were the number of lesions, maximum lesion diameter, lesion location, and lesion zone. We subsequently removed the inputs with low importance. Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting Tree (XGBoost) were employed. From receiver operating characteristic (ROC) curves, we determined Area Under the ROC Curve (AUC), the cut-off point, and sensitivity score (recall score) to evaluate the ML-model performance.

RESULTS

Twenty-four adenocarcinoma patients had a mean age of 70 ± 5.79 years, a mean PSA of 12.42 ± 6.67 ng/ml, a mean prostate volume of 46.49 ± 23.13 ml, and a mean PSAD of 0.31 ± 0.22 ng/ml² . Seventy-seven patients with benign tissue reports had a mean age of 66.39 ± 6.66 years, a mean PSA of 11.31 ± 7.50 ng/ml, a mean prostate volume of 65.25 ± 35.88 ml, and a mean PSAD of 0.19 ± 0.13 ng/ml² . On the validation cohort, XGBoost had the best AUC of 0.76, which considered 80% sensitivity and 72% specificity at a probability cutoff of 57%. The remaining possible ML models performed worse with lesser AUC. The worst was Naïve Bayes, with AUC of 0.50.

CONCLUSIONS

ML models facilitate PIRADS 3 patient selection for MRI/US fusion biopsies. ML could optimize how we use previously known clinical risk factors to their full potential.

Validation of an MRI-based prostate cancer prebiopsy Gleason score predictive nomogram

Background:

Gleason score grading is a cornerstone of risk stratification and management of patients with prostate cancer (PCa). In this work, we derive and validate a nomogram that uses prostate multiparametric magnetic resonance imaging (MP-MRI) and clinical patient characteristics to predict biopsy Gleason scores (bGS).

Materials and methods:

A predictive nomogram was derived from 143 men who underwent MP-MRI prior to any prostate biopsy and then validated on an independent cohort of 235 men from a different institution who underwent MP-MRI for PCa workup. Screen positive lesions were defined as lesions positive on T2W and DWI sequences on MP-MRI. Prostate specific antigen (PSA) density, number of screen positive lesions, and MRI suspicion were associated with PCa Gleason score on biopsy and were used to generate a predictive nomogram. The independent cohort was tested on the nomogram and the most likely bGS was noted.

Results:

The mean PSA in the validation cohort was 9.25ng/mL versus 6.8ng/mL in the original cohort (p = 0.001). The distribution of Gleason scores between the 2 cohorts were not significantly different (p = 0.7). In the original cohort of men, the most probable nomogram generated Gleason score agreed with actual pathologic bGS findings in 61% of the men. In the validation cohort, the most likely nomogram predicted bGS agreed with actual pathologic bGS 51% of the time. The nomogram correctly identified any PCa versus non-PCa 63% of the time and clinically significant (Gleason score ≥ 7) PCa 69% of the time. The negative predictive value for clinically significant PCa using this prebiopsy nomogram was 74% in the validation group.

Conclusions:

A preintervention nomogram based on PSA and MRI findings can help narrow down the likely pathologic finding on biopsy. Validation of the nomogram demonstrated a significant ability to correctly identify the most likely bGS. This feasibility study demonstrates the potential of a prebiopsy prediction of bGS and based on the high negative predictive value, identification of men who may not need biopsies, which could impact future risk stratification for PCa.

Impact of preoperative systemic immune-inflammation Index on oncologic outcomes in bladder cancer patients treated with radical cystectomy

PURPOSE

To investigate the predictive and prognostic value of the preoperative systemic immune-inflammation index (SII) in patients undergoing radical cystectomy (RC) for clinically non-metastatic urothelial cancer of the bladder (UCB).

METHODS

Overall, 4,335 patients were included, and the cohort was stratified in two groups according to SII using an optimal cut-off determined by the Youden index. Uni- and multivariable logistic and Cox regression analyses were performed, and the discriminatory ability by adding SII to a reference model based on available clinicopathologic variables was assessed by area under receiver operating characteristics curves (AUC) and concordance-indices. The additional clinical net-benefit was assessed using decision curve analysis (DCA).

RESULTS

High SII was observed in 1879 (43%) patients. On multivariable preoperative logistic regression, high SII was associated with lymph node involvement (LNI; P = 0.004), pT3/4 disease (P <0.001), and non-organ confined disease (NOCD; P <0.001) with improvement of AUCs for predicting LNI (P = 0.01) and pT3/4 disease (P = 0.01). On multivariable Cox regression including preoperative available clinicopathologic values, high SII was associated with recurrence-free survival (P = 0.028), cancer-specific survival (P = 0.005), and overall survival (P = 0.006), without improvement of concordance-indices. On DCAs, the inclusion of SII did not meaningfully improve the net-benefit for clinical decision-making in all models.

CONCLUSION

High preoperative SII is independently associated with pathologic features of aggressive disease and worse survival outcomes. However, it did not improve the discriminatory margin of a prediction model beyond established clinicopathologic features and failed to add clinical benefit for decision making. The implementation of SII as a part of a panel of biomarkers in future studies might improve decision-making.

Clinical Impact of the Predict Prostate Risk Communication Tool in Men Newly Diagnosed with Nonmetastatic Prostate Cancer: A Multicentre Randomised Controlled Trial

BACKGROUND

Predict Prostate is a freely available online personalised risk communication tool for men with nonmetastatic prostate cancer. Its accuracy has been assessed in multiple validation studies, but its clinical impact among patients has not hitherto been assessed.

OBJECTIVE

To assess the impact of the tool on patient decision-making and disease perception.

DESIGN, SETTING, AND PARTICIPANTS

A multicentre randomised controlled trial was performed across eight UK centres among newly diagnosed men considering either active surveillance or radical treatment. A total of 145 patients were included between 2018 and 2020, with median age 67 yr (interquartile range [IQR] 61-72) and prostate-specific antigen 6.8 ng/ml (IQR 5.1-8.8).

INTERVENTION

Participants were randomised to either standard of care (SOC) information or SOC and a structured presentation of the Predict Prostate tool.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Validated questionnaires were completed by assessing the impact of the tool on decisional conflict, uncertainty, anxiety, and perception of survival.

RESULTS AND LIMITATIONS

Mean Decisional Conflict Scale scores were 26% lower in the Predict Prostate group (mean = 16.1) than in the SOC group (mean = 21.7; p = 0.027). Scores on the "support", "uncertainty", and "value clarity" subscales all favoured Predict Prostate (all p < 0.05). There was no significant difference in anxiety scores or final treatment selection between the two groups. Patient perception of 15-yr prostate cancer-specific mortality (PCSM) and overall survival benefit from radical treatment were considerably lower and more accurate among men in the Predict Prostate group (p < 0.001). In total, 57% of men reported that the Predict Prostate estimates for PCSM were lower than expected, and 36% reported being less likely to select radical treatment. Over 90% of patients in the intervention group found it useful and 94% would recommend it to others.

CONCLUSIONS

Predict Prostate reduces decisional conflict and uncertainty, and shifts patient perception around prognosis to be more realistic. This randomised trial demonstrates that Predict Prostate can directly inform the complex decision-making process in prostate cancer and is felt to be useful by patients. Future larger trials are warranted to test its impact upon final treatment decisions.

PATIENT SUMMARY

In this national study, we assessed the impact of an individualised risk communication tool, called Predict Prostate, on patient decision-making after a diagnosis of localised prostate cancer. Men were randomly assigned to two groups, which received either standard counselling and information, or this in addition to a structured presentation of the Predict Prostate tool. Men who saw the tool were less conflicted and uncertain in their decision-making, and recommended the tool highly. Those who saw the tool had more realistic perception about their long-term survival and the potential impact of treatment upon this.

TAKE HOME MESSAGE

The use of an individualised risk communication tool, such as Predict Prostate, reduces patient decisional conflict and uncertainty when deciding about treatment for nonmetastatic prostate cancer. The tool leads to more realistic perceptions about survival outcomes and prognosis.

Detection of fusion gene transcripts in the blood samples of prostate cancer patients

Prostate cancer remains one of the most lethal cancers for men in the United States. The study aims to detect fusion transcripts in the blood samples of prostate cancer patients. We analyzed nine fusion transcripts including MAN2A1-FER, SLC45A2-AMACR, TRMT11-GRIK2, CCNH-C5orf30, mTOR-TP53BP1, KDM4-AC011523.2, TMEM135-CCDC67, LRRC59-FLJ60017 and Pten-NOLC1147 in the blood samples from 147 prostate cancer patients and 14 healthy individuals, using Taqman RT-PCR and Sanger's sequencing. Similar analyses were also performed on 25 matched prostate cancer samples for matched-sample evaluation. Eighty-two percent blood samples from the prostate cancer patients were positive for MAN2A1-FER transcript, while 41.5% and 38.8% blood samples from the prostate cancer patients were positive for SLC45A2-AMACR and Pten-NOLC1, respectively. CCNH-c5orf30 and mTOR-TP53BP1 had low detection rates, positive in only 5.4% and 4% of the blood samples from the prostate cancer patients. Only 2 blood samples were positive for KDM4B-AC011523.2 transcript. Overall, 89.8% patients were positive for at least one fusion transcript in their blood samples. The statistical analysis showed varied sensitivity of fusion transcript detection in the blood based on the types of fusions. In contrast, the blood samples from all healthy individuals were negative for the fusion transcripts. Detection of fusion transcripts in the blood samples of the prostate cancer patients may be a fast and cost-effective way to detect prostate cancer.

The role of lymph node dissection in salvage radical prostatectomy for patients with radiation recurrent prostate cancer

PURPOSE

To examine the effect of lymph node dissection on the outcomes of patients who underwent salvage radical prostatectomy (SRP).

MATERIAL AND METHODS

We retrospectively reviewed data from radiation-recurrent patients with prostate cancer (PCa) who underwent SRP from 2000-2016. None of the patients had clinical lymph node involvement before SRP. The effect of the number of removed lymph nodes (RLNs) and the number of positive lymph nodes (PLNs) on biochemical recurrence (BCR)-free survival, metastases free survival, and overall survival (OS) was tested in multivariable Cox regression analyses.

RESULTS

About 334 patients underwent SRP and pelvic lymph node dissection (PLND). Lymph node involvement was associated with increased risk of BCR (p < .001), metastasis (p < .001), and overall mortality (p = .006). In a multivariable Cox regression analysis, an increased number of RLNs significantly lowered the risk of BCR (hazard ratio [HR] 0.96, p = .01). In patients with positive lymph nodes, a higher number of RLNs and a lower number of PLNs were associated with improved freedom from BCR (HR 0.89, p = .001 and HR 1.34, p = .008, respectively). At a median follow-up of 23.9 months (interquartile range, 4.7-37.7), neither the number of RLNs nor the number of PLNs were associated with OS (p = .69 and p = .34, respectively).

CONCLUSION

Pathologic lymph node involvement increased the risk of BCR, metastasis and overall mortality in radiation-recurrent PCa patients undergoing SRP. The risk of BCR decreased steadily with a higher number of RLNs during SRP. Further research is needed to support this conclusion and develop a precise therapeutic adjuvant strategy based on the number of RLNs and PLNs.

Salvage Radical Prostatectomy for Radio-Recurrent Prostate Cancer: An Updated Systematic Review of Oncologic, Histopathologic and Functional Outcomes and Predictors of Good Response

A valid treatment option for recurrence after definite radiotherapy (RT) for localized prostate cancer (PC) is salvage radical prostatectomy (SRP). However, data on SRP are scarce, possibly resulting in an underutilization. A systematic review was performed using MEDLINE (Pubmed), Embase, and Web of Science databases including studies published between January 1980 and April 2020. Overall, 23 English language articles including a total number of 2323 patients were selected according to PRISMA criteria. The overall median follow-up was 37.5 months (IQR 35.5–52.5). Biochemical-recurrence (BCR)-free probability ranged from 34% to 83% at five years, respectively, and from 31% to 37% at 10 years. Cancer specific survival (CSS) and overall survival (OS) ranged from 88.7% to 98% and 64% to 95% at five years and from 72% to 83% and 65% to 72% at 10 years, respectively. Positive surgical margins ranged from 14% to 45.8% and pathologic organ-confined disease was reported from 20% to 57%. The rate of pathologic > T2-disease ranged from 37% to 80% and pN1 disease differed between 0% to 78.4%. Pre-SRP PSA, pre-SRP Gleason Score (GS), pathologic stage after SRP, and pathologic lymph node involvement seemed to be the strongest prognostic factors for good outcomes. SRP provides accurate histopathological and functional outcomes, as well as durable cancer control. Careful patient counseling in a shared decision-making process is recommended.

The prognostic value of sarcopenia in patients with prostate cancer: a systematic review

PURPOSE OF REVIEW

Sarcopenia is known to affect perioperative and oncologic outcomes in patients with different urological malignancies. Nevertheless, the use of pretreatment sarcopenia as a predictor of clinical outcomes in patients with prostate cancer is still poorly studied. Therefore, we aimed to conduct a systematic review summarizing the available evidence and identifying the prognostic value of sarcopenia in prostate cancer patients.

RECENT FINDINGS

Sarcopenia was not predictive of biochemical recurrence in patients treated with radical prostatectomy. However, it was associated with worse long-term survival outcomes as well as the likelihood of developing postoperative complications after radical prostatectomy. In the context of radiotherapy, sarcopenia was a predictive factor for overall survival. In patients with hormone-sensitive prostate cancer treated with androgen deprivation, sarcopenia was associated with overall and cancer-specific survival. In patients with castration-resistant prostate cancer, sarcopenia was associated with poorer tolerance to docetaxel-based chemotherapy.

SUMMARY

Most studies found sarcopenia to be a predictor for worse perioperative and oncologic outcomes in patients treated with radical surgery, radiotherapy, or systemic therapy for prostate cancer. However, there is significant heterogeneity across the studies in terms of sarcopenia definition. For a better understanding of the prognostic value of sarcopenia in patients with prostate cancer, large-scale prospective studies are needed.

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Background

Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication.

Methods

In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17).

Results

Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively.

Conclusions

Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Gleason grading is the process by which pathologists assess the morphology of prostate tumors. The assigned Grade Group tells us about the likely clinical course of people with prostate cancer and helps doctors to make decisions on treatment. The process is complex and subjective, with frequent disagreement amongst pathologists. In this study, we develop and evaluate an approach to Gleason grading based on artificial intelligence, rather than pathologists’ assessment, to predict risk of dying of prostate cancer. Looking back at tumors and data from 2,807 people diagnosed with prostate cancer, we find that our approach is better at predicting outcomes compared to grading by pathologists alone. These findings suggest that artificial intelligence might help doctors to accurately determine the probable clinical course of people with prostate cancer, which, in turn, will guide treatment.

Wulczyn et al. utilise a deep learning-based Gleason grading model to predict prostate cancer-specific mortality in a retrospective cohort of radical prostatectomy patients. Their model enables improved risk stratification compared to pathologists’ grading and demonstrates the potential for computational pathology in the management of prostate cancer.

Prognostic value of the pre-operative serum albumin to globulin ratio in patients with non-metastatic prostate cancer undergoing radical prostatectomy

PURPOSE

To evaluate the potential predictive value of the preoperative serum albumin to globulin ratio (AGR) for oncological outcomes in patients treated with radical prostatectomy (RP) for clinically non-metastatic prostate cancer (PCa).

METHODS

Pre-operative AGR was assessed in a multi-institutional cohort of 6041 patients treated with RP. Logistic regression analyses were performed to assess the association of the AGR with advanced disease. We performed Cox regression analyses to determine the relationship between AGR and biochemical recurrence (BCR).

RESULTS

The optimal cut-off value was determined to be 1.31 according to receiver operating curve analysis. Compared to patients with a higher AGR, those with a lower preoperative AGR had worse BCR-free survival (P < 0.01) in the Kaplan-Meier analysis. Pre- and post-operative multivariable models that adjusted for the effects of established clinicopathologic features, confirmed its independent association with BCR [hazard ratio (HR) 1.52, 95% confidence interval (CI) 1.31-1.75, P < 0.01, HR 1.55, 95% CI 1.34-1.79, P < 0.01, respectively]. However, the addition of AGR to established prognostic models did not improve their discrimination.

CONCLUSION

While AGR is significantly associated with BCR, in the present study, the clinical impact of AGR was not large enough to affect patient management. Longer follow-up is necessary to observe the true effect of AGR.

Systemic therapies for metastatic hormone-sensitive prostate cancer: network meta-analysis

Objectives

To perform a systematic review and network meta‐analysis to compare the efficacy and safety of currently available treatments for the management of metastatic hormone‐sensitive prostate cancer (mHSPC), as there has been a paradigm shift with the use of next‐generation androgen receptor inhibitors (ARIs) and docetaxel.

Methods

Multiple databases were searched for articles published before May 2020 according to the Preferred Reporting Items for Systematic Review and Meta‐analysis extension statement for network meta‐analysis. Studies comparing overall/progression‐free survival (OS/PFS) and/or adverse events (AEs) in patients with mHSPC were eligible.

Results

Nine studies (N = 9960) were selected, and formal network meta‐analyses were conducted. Abiraterone (hazard ratio [HR] 0.83, 95% credible interval [CrI] 0.76–0.90), docetaxel (HR 0.90, 95% CrI 0.82–0.98), and enzalutamide (HR 0.85, 95% CrI 0.73–0.99) were associated with significantly better OS than androgen‐deprivation therapy (ADT), and abiraterone emerged as the best option. Abiraterone (HR 0.71, 95% CrI 0.67–0.76), apalutamide (HR 0.73, 95% CrI 0.65–0.81), docetaxel (HR 0.84, 95% CrI 0.78–0.90), and enzalutamide (HR 0.67, 95% CrI 0.63–0.71) were associated with significantly better PFS than ADT, and enzalutamide emerged as the best option. Abiraterone (HR 0.85, 95% CrI 0.78–0.93), apalutamide (HR 0.87, 95% CrI 0.77–0.98), and enzalutamide (HR 0.80, 95% CrI 0.73–0.88) were significantly more effective than docetaxel. Regarding AEs, apalutamide was the likely best option among the three ARIs. In patients with low‐volume mHSPC, enzalutamide was the best option in terms of OS and PFS.

Conclusions

All three ARIs are effective therapies for mHSPC; apalutamide was the best tolerated. All three seemed more effective than docetaxel. These findings may facilitate individualised treatment strategies and inform future comparative trials.

Machine learning applications in radiation oncology

Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

Impact of the preoperative modified glasgow prognostic score on disease outcome after radical cystectomy for urothelial carcinoma of the bladder

BACKGROUND

To investigate the predictive and prognostic value of the preoperative modified Glasgow Prognostic Score (mGPS) in patients with urothelial carcinoma of the bladder (UCB) treated with radical cystectomy (RC).

METHODS

We conducted a retrospective analysis of an established multicenter database consisting of 4,335 patients who were treated with RC +/- adjuvant chemotherapy for UCB between 1979 and 2012. The mGPS of each patient was calculated on the basis of preoperative serum C-reactive protein and albumin. Uni- and multivariable logistic and Cox regression analyses were performed. The discriminatory ability of the models was assessed by calculating the area under receiver operating characteristics curves (AUC) and concordance-indices (C-Index). The additional clinical net-benefit was assessed using the decision curve analysis (DCA).

RESULTS

A mGPS of 0, 1, and 2 was observed in 3,158 (72.8%), 1,020 (23.5%), and 157 (3.6%) patients, respectively. On multivariable logistic regression analyses, mGPS of 1 or 2 were associated with an increased risk of pT3/4 disease at RC (OR 1.25, p=0.004 and OR 2.58, p<0.001, respectively) and/or lymph node metastasis (OR 1.7, p<0.001 and OR 3.9, p<0.001, respectively). Addition of the mGPS to a predictive model based on preoperatively available variables improved its accuracy for prediction of lymph node metastasis (change of AUC +3.7%, p<0.001). On multivariable Cox regression analyses, mGPS of 1 or 2 remained associated with worse recurrence-free survival (HR 1.14, p=0.03 and HR 1.89 p<0.001, respectively), cancer-specific survival (HR 1.16, p=0.032 and HR 2.1, p<0.001, respectively) and overall survival (HR 1.5, p=0.007 and HR 1.92 p<0.001, respectively) compared to mGPS of 0. The additional discriminatory ability of the mGPS for prognosis of survival outcomes in separate models that included either established pre- or postoperative variables did not improve the C-Index by a prognostically relevant degree (change of C-Index <2% for all models). On DCA, the inclusion of the mGPS did not meaningfully improve the net-benefit for clinical decision-making regarding survival outcomes.

CONCLUSIONS

We confirmed that an elevated mGPS is an independent risk factor for non-organ confined disease and poor survival outcomes in patients with UCB undergoing RC. However, the mGPS showed little value in improving the discriminatory ability of predictive and prognostic models that relied on either pre- or postoperative clinicopathological variables. The discriminatory ability of this biomarker in the age of immunotherapy warrants further evaluation.

Impact of systemic Immune-inflammation Index on oncologic outcomes in patients treated with radical prostatectomy for clinically nonmetastatic prostate cancer

PURPOSE

To evaluate the predictive and prognostic value of the Systemic Immune-inflammation Index (SII) in a large cohort of patients treated with radical prostatectomy (RP) for clinically non-metastatic prostate cancer (PCa).

METHODS

We retrospectively analyzed our multicenter database comprising 6,039 consecutive patients. The optimal preoperative SII cut-off value was assessed with the Youden index calculated on a time-dependent receiver operating characteristic (ROC) curve. Logistic regression and Cox regression analyses were used to investigate the association of SII with pathologic features and biochemical recurrence (BCR), respectively. The discriminatory ability of the models was evaluated by calculating the concordance-indices (C-Index). The clinical benefit of the implementation of SII in clinical decision making was assessed using decision curve analysis (DCA).

RESULTS

Patients with high preoperative SII (≥ 620) were more likely to have adverse clinicopathologic features. On multivariable logistic regression analysis, high preoperative SII was independently associated with extracapsular extension (odds ratio [OR] 1.16, P = 0.041), non-organ confined disease (OR 1.18, P = 0.022), and upgrading at RP (OR 1.23, P < 0.001). We built two Cox regression models including preoperative and postoperative variables. In the preoperative multivariable model, high preoperative SII was associated with BCR (hazard ratio [HR] 1.34, 95% CI 1.15-1.55, P < 0.001). In the postoperative multivariable model, SII was not associated with BCR (P = 0.078). The addition of SII to established models did not improve their discriminatory ability nor did it increase the clinical net benefit on DCA.

CONCLUSION

In men treated with RP for clinically nonmetastatic PCa, high preoperative SII was statistically associated with an increased risk of adverse pathologic features at RP as well as BCR. However, it did not improve the predictive accuracy and clinical value beyond that obtained by current predictive and prognostic models. SII together with a panel of complementary biomarkers is praised to help guide decision-making in clinically nonmetastatic PCa.

Validation of the new STAR-CAP prognostic group staging system in prostate cancer patients treated with radiation therapy

PURPOSE

To externally validate the STAR-CAP prognostic system for prostate cancer (PCa) and compare it to the CAPRA score to predict for biochemical recurrence (BCR) after radiation therapy (RTx).

METHODS

We included patients treated with RTx between 2002 and 2021 for non-metastatic PCa at our institution. BCR was defined based on Phoenix criteria. The 5-year BCR-free survival was assessed by univariable Kaplan-Meier analyses and log-rank test. Multivariable Cox regression models tested the independent association of each model for BCR. Performance of both models to predict 5-year BCR-free survival was assessed using the area under the curve (AUC).

RESULTS

The 2768 patients included were treated with high dose rate brachytherapy (13.3%) as a boost to external beam radiation therapy (EBRT), low dose rate seed brachytherapy (50.4%) or EBRT alone (35.9%). 14.4% of patients received concomitant androgen deprivation therapy (ADT). 222 patients experienced BCR (8%), with a median follow-up of 56 months. The 5-year BCR-free survival ranged from 88 (high risk) to 96% (low risk) in the STAR-CAP classification, and from 87 (high risk) to 97% (low risk) in the CAPRA system (p < 0.0001). Multivariate analyses, adjusted for ADT and type of treatment, confirmed the intrinsic ability of risk stratifications within each system to predict BCR (p < 0.001). Finally, AUC for the 5-year BCR prediction was 0.65 for STAR-CAP and 0.68 for CAPRA.

CONCLUSION

Both CAPRA and STAR-CAP prognostic group staging systems provide sufficient stratification and their predictive ability for 5-year BCR-free survival is comparable, with a small advantage for CAPRA (3%).

Role of systemic immune-inflammation index in patients treated with salvage radical prostatectomy

Purpose

To examine the predictive and prognostic value of preoperative Systemic Immune-inflammation Index (SII) in patients with radio-recurrent prostate cancer (PCa) treated with salvage radical prostatectomy (SRP).

Materials and methods

This multicenter retrospective study included 214 patients with radio-recurrent PCa, treated with SRP between 2007 and 2015. SII was measured preoperatively (neutrophils × platelets/lymphocytes) and the cohort was stratified using optimal cut-off. Uni- and multivariable logistic and Cox regression analyses were performed to evaluate the predictive and prognostic value of SII as a preoperative biomarker.

Results

A total of 81 patients had high preoperative SII (≥ 730). On multivariable logistic regression modeling, high SII was predictive for lymph node metastases (OR 3.32, 95% CI 1.45–7.90, p = 0.005), and non-organ confined disease (OR 2.55, 95% CI 1.33–4.97, p = 0.005). In preoperative regression analysis, high preoperative SII was an independent prognostic factor for cancer-specific survival (CSS; HR 10.7, 95% CI 1.12–103, p = 0.039) and overall survival (OS; HR 8.57, 95% CI 2.70–27.2, p < 0.001). Similarly, in postoperative multivariable models, SII was associated with worse CSS (HR 22.11, 95% CI 1.23–398.12, p = 0.036) and OS (HR 5.98, 95% CI 1.67–21.44, p = 0.006). Notably, the addition of SII to preoperative reference models improved the C-index for the prognosis of CSS (89.5 vs. 80.5) and OS (85.1 vs 77.1).

Conclusions

In radio-recurrent PCa patients, high SII was associated with adverse pathological features at SRP and survival after SRP. Preoperative SII could help identify patients who might benefit from novel imaging modalities, multimodal therapy or a closer posttreatment surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-021-03715-4.

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.

Aim for Clinical Utility, Not Just Predictive Accuracy

Supplemental Digital Content is available in the text.

The predictions from an accurate prognostic model can be of great interest to patients and clinicians. When predictions are reported to individuals, they may decide to take action to improve their health or they may simply be comforted by the knowledge. However, if there is a clearly defined space of actions in the clinical context, a formal decision rule based on the prediction has the potential to have a much broader impact. The use of a prediction-based decision rule should be formalized and preferably compared with the standard of care in a randomized trial to assess its clinical utility; however, evidence is needed to motivate such a trial. We outline how observational data can be used to propose a decision rule based on a prognostic prediction model. We then propose a framework for emulating a prediction driven trial to evaluate the clinical utility of a prediction-based decision rule in observational data. A split-sample structure is often feasible and useful to develop the prognostic model, define the decision rule, and evaluate its clinical utility. See video abstract at, http://links.lww.com/EDE/B656.

Prognostic Significance of Prostate-Specific Antigen Persistence after Radical Prostatectomy: A Systematic Review and Meta-Analysis

We performed a systematic review and meta-analysis to assess the prognostic value of prostate-specific antigen (PSA) persistence 4-8 weeks after radical prostatectomy (RP) in patients with prostate cancer, using studies from Medline, Scopus, and Cochrane Library, on 10 October 2020. Studies were eligible if they compared patients with postoperative PSA persistence 4-8 weeks after RP to those without such persistence to assess the value of PSA persistence in prognosticating biochemical recurrence (BCR), disease recurrence, cancer-specific mortality (CSM), and overall mortality (OM) by multivariable analysis. Our review and analysis included nine studies published between 2008 and 2019 with 14,455 patients. Of those studies, 12.0% showed postoperative PSA persistence. PSA persistence was associated with BCR (HR: 4.44, 95% CI: 2.84-6.93), disease recurrence (HR: 3.43, 95% CI: 1.62-7.25), and CSM (HR: 2.32, 95% CI: 1.83-2.95). We omitted meta-analysis on the association of PSA persistence with OM due to an insufficient number of studies. PSA persistence was associated with disease recurrence in a sub-group of patients with pathological nodal involvement (HR: 5.90, 95% CI: 3.76-9.24). Understanding detection of PSA persistence at 4-8 weeks after RP might be useful for patient counseling, follow-up scheduling, and clinical decision-making regarding adjuvant therapies.

Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model

After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.

Retrospective cohort study evaluating clinical, biochemical and pharmacological prognostic factors for prostate cancer progression using primary care data

OBJECTIVES

To confirm the association of previously reported prognostic factors with future progression of localised prostate cancer using primary care data and identify new potential prognostic factors for further assessment in prognostic model development and validation.

DESIGN

Retrospective cohort study, employing Cox proportional hazards regression controlling for age, prostate specific antigen (PSA), and Gleason score, was stratified by diagnostic stage.

SETTING

Primary care in England.

PARTICIPANTS

Males with localised prostate cancer diagnosedbetween 01/01/1987 and 31/12/2016 within the Clinical Practice ResearchDatalink database, with linked data from the National Cancer Registration andAnalysis Service and Office for National Statistics.

PRIMARY AND SECONDARY OUTCOMES

Primary outcome measure was prostate cancer mortality. Secondary outcome measures were all-cause mortality and commencing systemic therapy. Up-staging after diagnosis was not used as a secondary outcome owing to significant missing data.

RESULTS

10 901 men (mean age 74.38±9.03 years) with localised prostate cancer were followed up for a mean of 14.12 (±6.36) years. 2331 (21.38%) men underwent systemic therapy and 3450 (31.65%) died, including 1250 (11.47%) from prostate cancer. Factors associated with an increased risk of prostate cancer mortality included age; high PSA; current or ex-smoker; ischaemic heart disease; high C reactive protein; high ferritin; low haemoglobin; high blood glucose and low albumin.

CONCLUSIONS

This study identified several new potential prognostic factors for prostate cancer progression, as well as confirming some known prognostic factors, in an independent primary care data set. Further research is needed to develop and validate a prognostic model for prostate cancer progression.

Functional Outcomes after Local Salvage Therapies for Radiation-Recurrent Prostate Cancer Patients: A Systematic Review

PURPOSE

To assess the rate and severity of functional outcomes after salvage therapy for radiation recurrent prostate cancer.

METHODS

This systematic review of the MEDLINE/PubMed database yielded 35 studies, evaluating salvage radical prostatectomy (RP), brachytherapy (BT), high-intensity focal ultrasound (HIFU) and cryotherapy (CT) after failure of primary radiation therapy. Data on pre- and post-salvage rates and severity of functional outcomes (urinary incontinence, erectile dysfunction, and lower urinary tract symptoms) were collected from each study.

RESULTS

The rates of severe urinary incontinence ranged from 28-88%, 4.5-42%, 0-6.5%, 2.4-8% post salvage RP, HIFU, CT and BT, respectively. The rates of erectile dysfunction were relatively high reaching as much as 90%, 94.6%, 100%, 62% following RP, HIFU, CT and BT, respectively. Nonetheless, the high pre-salvage rates of ED preclude accurate estimation of the effect of salvage therapy. There was an increase in the median IPSS following salvage HIFU, BT and CT ranging from 2.5-3.4, 3.5-12, and 2, respectively. Extended follow-up showed a return-to-baseline IPSS in a salvage BT study. The reported data suffer from selection, reporting, publication and period of study biases, making inter-study comparisons inappropriate.

CONCLUSIONS

local salvage therapies for radiation recurrent PCa affect continence, lower urinary tract symptoms and sexual functions. The use of local salvage therapies may be warranted in the setting of local disease control, but each individual decision must be made with the informed patient in a shared decision working process.

Impact of preoperative serum albumin-globulin ratio on disease outcome after radical cystectomy for urothelial carcinoma of the bladder

INTRODUCTION

The Albumin-Globulin Ratio (AGR; albumin/total protein - albumin) has been associated with oncological outcome in various malignancies. However, its role in urothelial carcinoma of the bladder (UCB) has not been clearly established. In this study, we assessed the association of preoperative AGR (pAGR) with survival in patients who underwent radical cystectomy (RC) for UCB.

MATERIAL AND METHODS

We conducted a retrospective analysis of an established multicenter database of 4.335 patients who were treated with RC for UCB. The cohort was divided into 2 groups according to the pAGR status. Binominal logistic regression as well as uni- and multivariable Cox regression analyses were used. The predictive value of the models was assessed by calculating receiver operating characteristics curves and concordance-indices (C-Index). The additional clinical value was assessed using the decision curve analysis (DCA).

RESULTS

Overall, 1.670 patients (38.5%) had a low pAGR. On multivariable logistic regression analyses, low pAGR was associated with an increased risk of ≥pT3 disease at RC (odds ratio [OR] 1.15, 95% confidence interval [CI] 1.01-1.31, P= 0.04). On multivariable Cox regression analyses, low pAGR remained associated with worse recurrence-free survival (RFS, HR 1.24, 95% CI 1.1-1.37, P< 0.001), cancer-specific survival (CSS, HR 1.23, 95% CI 1.1-1.38, P< 0.001) and overall survival (OS, HR 1.17, 95% CI 1.07-1.28, P< 0.001). The addition of pAGR to multiple prognostic models that were respectively fitted for clinical and postoperative variables did not improve the predictive accuracy.

CONCLUSION

pAGR status is an independent predictor of ≥pT3 disease, therefore it could help identify patients who have a higher likelihood to benefit from neoadjuvant systemic therapy. While pAGR was independently associated with RFS, CSS, and OS, it did not improve the predictive accuracy and clinical value beyond obtained by information already available. The predictive value of this biomarker in the age of immunotherapy needs further evaluation.

The distribution of refraction by age and gender in a non-myopic Chinese children population aged 6-12 years

Background

The Prevalence of myopia is increasing in China. This study aimed to explore the distribution of spherical equivalent (SE) and its association with age, body mass index (BMI), gender in a non-myopic Chinese children population aged 6 to 12 years.

Methods

A total of 6362 students were recruited for ophthalmological investigation. Demographic and myopia related behavioral information was collected. SE value was measured by the Topcon RM-8900 or KR-800autorefractors. Potential independent risk factors were determined with Odds Ratio (OR) and 95% Confidence Interval (CI) by logistic regression analysis. We further constructed the nomogram model to predict future onset of myopia.

Results

Among the study population, 3900 (61.3%) were non-myopic. The prevalence of myopia is 38.0% for boys and 39.5% for girls. The average SE values were 0.50 ± 0.70 D for boys and 0.60 ± 0.80 D for girls. The mean SE values decreased with age, and the value of height and BMI took on a stable trend. Threshold values for myopia varied across age groups and gender. Paternal myopia (OR: 1.22, 95%CI: 1.01–1.48), near-work activities on weekends (2.56, 1.17–5.61), and outdoor activities (0.68, 0.54–0.86) were associated with potential myopic in students.

Conclusion

A series of age-gender based SE threshold values were established to predict myopia in Chinese children aged 6 to 12 years. High risk factors for myopia included paternal myopia, near-work activities on weekends, and outdoor activities. Countermeasures are encouraged to reverse the increasing trend of myopia in children.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-020-01709-1.

Prognostic value of preoperative albumin to globulin ratio in patients treated with salvage radical prostatectomy for radiation recurrent prostate cancer

BACKGROUND

Serum albumin-to-globulin ratio (AGR) has been shown to be associated with poor prognosis in different malignancies. In this study we aimed to evaluate the predictive value of preoperative AGR for oncological outcomes in patients with radiation recurrent prostate cancer (PCa) treated with salvage radical prostatectomy (SRP).

METHODS

A retrospective review of 214 consecutive patients with radiation recurrent PCa who underwent SRP at five referral centers. Levels of albumin and globulin were obtained before SRP and used to calculate the preoperative AGR level. The optimal cut off value of preoperative AGR was 1.4. Univariable and multivariable Cox regression analyses were performed.

RESULTS

Overall 89 (41.6%) patients had a low preoperative AGR. Low serum AGR was associated with biochemical recurrence (BCR) in univariable Cox regression analysis (HR 1.60, 95%CI 1.06-2.43, P=0.026). When adjusted for the effects of established preoperative and postoperative clinicopathologic confounders in different multivariable Cox regression models, this association did not retain its statistical significance. Moreover, preoperative AGR was not associated with metastasis free survival (P=0.21), overall survival (P=0.91) or cancer specific survival (P=0.61).

CONCLUSIONS

In patients with radiation recurrent PCa undergoing SRP, low preoperative AGR was associated with the risk of BCR only in univariable analysis. There was no association with metastasis or survival outcomes. Further studies are needed to evaluate this biomarker in the setting of primary PCa and to identify the patients most likely to benefit from a local therapy.

Association of De Ritis ratio with oncological outcomes in patients with non-muscle invasive bladder cancer (NMIBC)

Purpose

The De Ritis ratio (aspartate aminotransferase/alanine aminotransferase, DRR) has been linked to oncological outcomes in several cancers. We aimed to assess the association of DRR with recurrence-free survival (RFS) and progression-free survival (PFS) in patients with non-muscle-invasive bladder cancer (NMIBC).

Methods

We conducted a retrospective analysis of 1117 patients diagnosed with NMIBC originating from an established multicenter database. To define the optimal pretreatment DRR cut‐off value, we determined a value of 1.2 as having a maximum Youden index value. The overall population was therefore divided into two De Ritis ratio groups using this cut‐off (lower, < 1.2 vs. higher, ≥ 1.2). Univariable and multivariable Cox regression analyses were used to investigate the association of DRR with RFS and PFS. The discrimination of the model was evaluated with the Harrel’s concordance index (C-index).

Results

Overall, 405 (36%) patients had a DRR ≥ 1.2. On univariable Cox regression analysis, DRR was significantly associated with RFS (HR: 1.23, 95% CI 1.02–1.47, p = 0.03), but not with PFS (HR: 0.96, 95% CI 0.65–1.44, p = 0.9). On multivariable Cox regression analysis, which adjusted for the effect of established clinicopathologic features, DRR ≥ 1.2 remained significantly associated with worse RFS (HR:1.21, 95% CI 1.00–1.46, p = 0.04). The addition of DRR only minimally improved the discrimination of a base model that included established clinicopathologic features (C-index = 0.683 vs. C-index = 0.681). On DCA the inclusion of DRR did not improve the net-benefit of the prognostic model.

Conclusion

Despite the statistically significant association of the DRR with RFS in patients with NMIBC, it does not seem to add any prognostic or clinical benefit beyond that of currently available clinical factors.

Treatment of localized prostate cancer and use of nomograms among urologists in the West Africa sub-region

INTRODUCTION

there is a high incidence of prostate cancer among men of African descent. The disease tends to occur at an early age with a tendency to be aggressive. The objective was to determine the practice of urologists in the West African sub-region regarding treatment of localized prostate cancer, the use of nomograms and their perception of the usefulness of nomograms.

METHODS

this was a cross-sectional study that involved urologists practicing in the West African sub-region attending urology and surgery conferences of the "Société Internationale d´Urologie", West African college of surgeons and the Ghana association of urological surgeons. A structured questionnaire was used that sort to ascertain the treatment modalities used for localized prostate cancer and the use of nomograms in the sub-region. The study period spanned the years 2018 and 2019.

RESULTS

fifty-six urologists practicing in eleven West African countries responded. Fifty percent had been in practice for less than 5 years. Sixty eight percent (38/56) had been involved in the treatment of localized prostate cancer. Radical prostatectomy was widely available and the treatment modality most used 94.7% (36/38). Nomograms was used by 57.9% of them (22/38) with the Partin tables being the most commonly used nomogram (34.2%). No Locally developed nomogram for treatment of localized prostate cancer was identified.

CONCLUSION

radical prostatectomy is the commonest treatment modality used for the management of localized prostate cancer in the West Africa sub-region. Majority of the urologists used nomograms with the Partin tables being the most used.

Ion Channel Profiling in Prostate Cancer: Toward Cell Population-Specific Screening

In the last three decades, a growing number of studies have implicated ion channels in all essential processes of prostate carcinogenesis, including cell proliferation, apoptosis, migration, and angiogenesis. The changes in the expression of individual ion channels show a specific profile, making these proteins promising clinical biomarkers that may enable better molecular subtyping of the disease and lead to more rapid and accurate clinical decision-making. Expression profiles and channel function are mainly based on the tumoral tissue itself, in this case, the epithelial cancer cell population. To date, little data on the ion channel profile of the cancerous prostate stroma are available, even though tumor interactions with the microenvironment are crucial in carcinogenesis and each distinct population plays a specific role in tumor progression. In this review, we describe ion channel expression profiles specific for the distinct cell population of the tumor microenvironment (stromal, endothelial, neuronal, and neuroendocrine cell populations) and the technical approaches used for efficient separation and screening of these cell populations.

An up-to-date catalogue of urinary markers for the management of prostate cancer

PURPOSE OF REVIEW

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. Poor specificity and sensitivity of total PSA often results in over and sometimes underdetection/treatment. Therefore, more specific and sensitive biomarkers for the detection and monitoring especially of clinically significant PCa as well as treatment-specific markers are much sought after. In this field, urine has emerged as a promising noninvasive source of biomarkers.

RECENT FINDINGS

RNA-based biomarkers are the most extensively studied type of urinary nucleic acids. ERG-Score/MiPS (Mi-Prostate Score) and SelectMDx might be considered as additional parameters together with clinical and imaging modalities to decrease unnecessary biopsies. miR Sentinel Tests could make it possible to accurately detect the presence of cancer and to distinguish low-grade from high-grade disease. In men with previous negative biopsies, PCA3 may suggest the need to repeat biopsy.

SUMMARY

The definitive role of these markers and their clinical benefit needs future validation.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Early upregulation of AR and steroidogenesis enzyme expression after 3 months of androgen-deprivation therapy

Background

Androgen deprivation therapy (ADT) is a standard treatment for advanced prostate cancer (PCa). However, PCa recurrence and progression rates during ADT are high. Until now, there has been no evidence regarding when progression begins. This study evaluated the gene expression of intraprostatic androgen receptor (AR) and steroidogenic enzymes in the early stages of ADT.

Methods

Prostate tissue samples were taken from PCa patients with urinary retention who received ADT (ADT-PCa; n = 10) and were further subgrouped into ADT ≤12 months (n = 4) and ADT > 12 months (n = 6). The ADT-PCa tissues were then compared with BPH (n = 12) and primary (no treatment) PCa tissues (n = 16). mRNA for gene expression analysis of AR and steroidogenic enzymes was extracted from formalin-fixed paraffin embedded (FFPE) tissues and analyzed by real-time PCR. Protein expression was evaluated by immunohistochemistry with specific antibodies.

Results

AR gene expression was higher in the ADT-PCa group than in the BPH or primary PCa group. Both the ADT ≤12 and > 12 months subgroups had significantly higher relative gene expression levels of AR (p < 0.01 and 0.03, respectively) than the primary PCa group. In the ADT-PCa group, AR protein expression showed an increasing trend in the ADT ≤12 months subgroup and was significantly elevated in the ADT > 12 months subgroup compared with the PCa group (100%; p < 0.01). Half (50%) of the patients in the ADT ≤12 months subgroup were found to have upregulation of AR, and one showed upregulation beginning at 3 months of ADT. A trend toward elevated relative gene expression of SRD5A3 was also apparent in the ADT groups.

Conclusion

AR and steroidogenic enzymes are upregulated in ADT-PCa patients as early as 3 months, without PSA elevation. Steroidogenic enzymes, particularly SRD5A3, were also upregulated before PSA rose.

Personalized strategies in population screening for prostate cancer

This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment.